CN112927045A - Car rental credit data processing method, device and equipment - Google Patents

Car rental credit data processing method, device and equipment Download PDF

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CN112927045A
CN112927045A CN202110232915.5A CN202110232915A CN112927045A CN 112927045 A CN112927045 A CN 112927045A CN 202110232915 A CN202110232915 A CN 202110232915A CN 112927045 A CN112927045 A CN 112927045A
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user
data
car
deposit
renting
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袁丽娟
张博
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Guangzhou Lizhi Network Technology Co ltd
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Guangzhou Lizhi Network Technology Co ltd
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Abstract

The application relates to a method, a device and equipment for processing taxi renting credit data. The method comprises the following steps: acquiring historical order data of a user; analyzing according to the execution condition of the historical order data to obtain taxi renting credit data; and distributing different deposit payment strategies for renting the car for the user according to the car rental credit data. According to the scheme, the user can rent the car and take the car more conveniently, the desire of the user for setting up an order on the car renting platform is improved, and meanwhile the risk of the car renting platform can be reduced.

Description

Car rental credit data processing method, device and equipment
Technical Field
The application relates to the technical field of internet, in particular to a method, a device and equipment for processing taxi renting credit data.
Background
In order to meet the travel requirements of people, the online car renting platform is suitable for transportation. Tens of thousands of car renting products of each merchant are connected to the online car renting platform, and a consumer serves as a user to find out proper car renting products and self-driving peripheral products through the online car renting platform.
As tourism becomes a daily life style of people, the automatic driving tour of car renting out of the country gradually becomes an enthusiastic tourism style of people. More and more users may want to travel by themselves outdoors when traveling abroad, so that a proper overseas car renting product is selected through the online car renting platform. At present, when a overseas user gets a car from a store after setting up an order (called for short) on a car rental platform, a car bank is unwilling to provide the car to the user due to the fact that the credit card limit or the debit card does not have the number of deposit specified by the car bank, so that the user cannot rent the car, complain and initiate the order refund on the car rental platform, and the intention of the user for setting up the order on the car rental platform is reduced.
Therefore, the deposit processing method in the related art brings inconvenience to users when renting and taking cars.
Disclosure of Invention
In order to solve or partially solve the problems in the related art, the application provides a method, a device and equipment for processing taxi renting credit data, which can enable a user to rent and take a taxi more conveniently and improve the desire of the user to set up an order on a taxi renting platform.
The application provides in a first aspect a method for processing taxi renting credit data, comprising:
acquiring historical order data of a user;
analyzing according to the execution condition of the historical order data to obtain taxi renting credit data;
and distributing different deposit payment strategies for renting the car for the user according to the car rental credit data.
In one embodiment, before obtaining the historical order data of the user, the method further includes:
acquiring behavior data of an order set by a user;
analyzing according to the behavior data to obtain taxi renting risk data;
and allocating a vehicle renting deposit strategy for the user according to the vehicle renting risk data.
In one embodiment, the allocating a car rental deposit policy to the user according to the car rental risk data includes:
according to the taxi renting risk data, prompting that the credit risk is high, and allocating unopened deposit-free service for the user; or the like, or, alternatively,
and prompting that the credit risk is controllable according to the car renting risk data, and allocating deposit layered payment service for the user.
In one embodiment, the allocating different deposit payment strategies for renting vehicles to the users according to the vehicle rental credit data comprises:
displaying historical orders containing a refusal payment record according to the taxi credit data, determining that the credit level is low, and distributing a strategy of paying according to a preset deposit amount for a user; or the like, or, alternatively,
and according to the taxi credit data, displaying that the historical order does not contain a refusal payment record, and the number of times of the effective order is greater than or equal to the set number of times, determining that the credit level is high, and allocating a strategy for adjusting the payment of the deposit amount to the user.
In one embodiment, the policy for allocating adjustable deposit line payment to the user comprises:
if the average unit price of the order of the user is larger than the set amount, allocating a deposit-free payment strategy for the user; or the like, or, alternatively,
if the average unit price of the user order is less than or equal to the set amount and the car rental model is not the set type, allocating a deposit free payment strategy for the user; or the like, or, alternatively,
and if the average unit price of the order of the user is less than or equal to the set amount and the car renting type is the set type, distributing a strategy for reducing or avoiding the set amount according to the preset deposit amount for the user.
In one embodiment, the allocating different deposit payment strategies for renting vehicles to the users according to the vehicle rental credit data comprises:
according to the taxi credit data, displaying that the historical order does not contain a refusal payment record, and the number of valid orders is less than the set number, and determining the credit level, then:
if the car rental model is a set type, distributing a strategy of paying according to a preset deposit amount for the user; or the like, or, alternatively,
if the car rental model is not the set type, a strategy of paying according to a deposit amount larger than the average unit price is distributed to the user.
In one embodiment, the method further comprises:
and when the user is a new user, distributing a strategy of paying according to a preset deposit amount for the user.
A second aspect of the present application provides a car rental credit data processing apparatus, including:
the order data acquisition module is used for acquiring historical order data of the user;
the credit data module is used for analyzing according to the execution condition of the historical order data acquired by the order data acquisition module to obtain taxi renting credit data;
and the deposit strategy module is used for distributing different deposit payment strategies for renting the car for the user according to the car renting credit data obtained by the credit data module.
In one embodiment, the apparatus further comprises:
the behavior data module is used for acquiring behavior data of an order set by a user; analyzing according to the behavior data to obtain taxi renting risk data;
and the deposit strategy module distributes a deposit strategy for renting the car for the user according to the car renting risk data obtained by the behavior data module.
A third aspect of the present application provides an electronic device comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method as described above.
A fourth aspect of the present application provides a non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform a method as described above.
The technical scheme provided by the application can comprise the following beneficial effects:
according to the scheme provided by the application, analysis is carried out according to the execution condition of historical order data of a user to obtain taxi renting credit data; and then according to the car rental credit data, different deposit payment strategies for car rental are distributed to the users. Therefore, for different users, due to different car rental credit data, the car can be taken after the car rental or the deposit is required to reach the set amount uniformly, and different payment strategies such as deposit avoidance or deposit reduction can be flexibly allocated according to different car rental credit data of the users. Therefore, according to the scheme provided by the application, the user can rent the vehicle and take the vehicle more conveniently while controlling the credit risk, the problem that the user is difficult to take the vehicle is solved, and the desire of the user to set up an order on a vehicle renting platform is improved.
According to the technical scheme, before historical order data of the user are obtained, behavior data of the order set by the user can be obtained; analyzing according to the behavior data to obtain taxi renting risk data; and then allocating a vehicle renting deposit strategy for the user according to the vehicle renting risk data. Therefore, the platform can further evaluate the car renting risk of the user in advance according to the user behavior, and therefore reference is provided for determining the deposit strategy.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application, as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 is a schematic flow chart illustrating a method for processing rental car credit data according to an embodiment of the present application;
fig. 2 is another schematic flow chart of a rental car credit data processing method according to an embodiment of the present application;
fig. 3 is another schematic flow chart of a rental car credit data processing method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a rental car credit data processing apparatus according to an embodiment of the present application;
fig. 5 is another schematic structural diagram of the rental car credit data processing apparatus according to the embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device shown in an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While embodiments of the present application are illustrated in the accompanying drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
The deposit processing method in the related technology brings inconvenience to users when renting and taking cars. In view of the above problems, the embodiment of the application provides a method for processing car rental credit data, which can control credit risk, make car rental and car taking of a user more convenient, improve the willingness of the user to set an order on a car rental platform, and reduce the risk of the car rental platform.
The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for processing rental car credit data according to an embodiment of the present application.
Referring to fig. 1, the method includes:
in step S101, user history order data is acquired.
After the user logs in the account, all or part of historical order data of the user can be obtained from the database, for example, order data within a half year or a year is obtained.
In step S102, analysis is performed according to the execution condition of the history order data to obtain rental car credit data.
And analyzing the execution condition of the historical order data, wherein the analysis comprises analyzing whether a refusal payment record or a deferred payment record appears in the historical order.
In step S103, different deposit payment policies for car rental are assigned to the user according to the car rental credit data.
In the step, historical orders including a refusal payment record can be displayed according to taxi renting credit data, the credit level is determined to be low, and a strategy of paying according to a preset deposit amount is distributed to a user; or the like, or, alternatively,
and according to the taxi credit data, displaying that the historical order does not contain a refusal payment record, and the number of valid orders is greater than or equal to the set number, determining that the credit level is high, and allocating a strategy for adjusting the payment of the deposit amount to the user.
Wherein, the strategy of allocating adjustable deposit amount payment for the user can further comprise:
if the average unit price of the order of the user is larger than the set amount, allocating a deposit-free payment strategy for the user; or the like, or, alternatively,
if the average unit price of the order of the user is less than or equal to the set amount and the car rental model is not the set type, allocating a deposit-free payment strategy for the user; or the like, or, alternatively,
and if the average unit price of the order of the user is less than or equal to the set amount and the car renting type is the set type, distributing a strategy for reducing or avoiding the set amount according to the preset deposit amount for the user.
In this step, it is displayed according to the rental car credit data that the historical order does not include the refusal payment record, and the number of valid orders is less than the set number, and in determining the credit level, then: if the car rental model is a set type, distributing a strategy of paying according to a preset deposit amount for the user; or if the car rental model is not the set type, allocating a strategy of paying according to a deposit amount larger than the average unit price for the user.
According to the embodiment, the scheme provided by the application is that the execution condition of the historical order data of the user is analyzed to obtain the taxi renting credit data; and then according to the car rental credit data, different deposit payment strategies for car rental are distributed to the users. Therefore, for different users, due to different car rental credit data, the car can be taken after the car rental or the deposit is required to reach the set amount uniformly, and different payment strategies such as deposit avoidance or deposit reduction can be flexibly allocated according to different car rental credit data of the users. Therefore, according to the scheme provided by the application, the user can rent the vehicle and take the vehicle more conveniently while controlling the credit risk, the problem that the user is difficult to take the vehicle is solved, and the desire of the user to set up an order on a vehicle renting platform is improved.
Fig. 2 is another schematic flow chart of the method for processing rental car credit data according to the embodiment of the present application. The flow in fig. 2 further includes a process for risk control based on user behavior data.
Referring to fig. 2, the method includes:
in step S201, behavior data of the user setting up an order is acquired.
The behavior data of the order set up by the user can include the average residence time per page, the average order placing time, the product order placing path, the payment path and the like of the user.
In step S202, analysis is performed according to the behavior data to obtain car rental risk data.
In this step, a behavior exception rule may be preset, and whether the behavior data of the order set by the user is abnormal or not may be determined according to the behavior exception rule. For example, the average stay time per page, the average order placing time, the product order placing path, the payment path and the like of the user are checked to determine whether the behavior data of the order set by the user is abnormal, so as to obtain the car rental risk data.
In step S203, a car rental deposit policy is assigned to the user according to the car rental risk data.
The credit risk is high according to the taxi renting risk data, and the non-open deposit-free service is distributed to the user; or, according to the car rental risk data, prompting that the credit risk is controllable, and allocating deposit layered payment service for the user.
And analyzing according to the user behavior data, evaluating that the credit risk of the user is high when the user behavior data is abnormal, and not opening the deposit-free service for the user, so that the rejection risk of the subsequent user is reduced, and the risk of a car renting platform is also reduced.
In step S204, user history order data is acquired.
This step can be referred to the description in step S101, and is not described herein again.
In step S205, the execution condition of the historical order data is analyzed to obtain car rental credit data.
This step can be referred to the description in step S102, and is not described herein again.
In step S206, different deposit payment policies for car rental are assigned to the user according to the car rental credit data and the assigned deposit policy.
This step can be referred to the description in step S103, and is not described herein again.
According to the embodiment, the technical scheme of the application can also obtain the behavior data of the order set by the user before obtaining the historical order data of the user; analyzing according to the behavior data to obtain taxi renting risk data; and then allocating a car rental deposit strategy for the user according to the car rental risk data. Therefore, the platform can further evaluate the car renting risk of the user in advance according to the user behavior, and therefore reference is provided for determining the deposit strategy.
Fig. 3 is another schematic flow chart of the method for processing rental car credit data according to the embodiment of the present application. Fig. 3 describes the solution of the present application in more detail with respect to fig. 1 and 2.
According to the scheme provided by the embodiment of the application, risk identification is carried out on the on-line order (placing) behavior of the user, and credit layering is carried out on the historical order consumption condition and the refusal payment condition of the user; offering different levels of deposit packages, etc., for users of different credit levels. For example, when a user who selects an order for a deposit package arrives at a store in a vehicle for pickup, the deposit originally required by the vehicle may not be paid or frozen. The existing transaction data of the user can be utilized to carry out credit layering initialization processing on the user in the database, and behavior abnormity rules can be preset, for example, whether the behavior data of the user is abnormal or not is judged according to the average residence time of each page, the average order placing time, the product order placing path, the payment path and the like of the user; by setting users with different credit levels, the gradient logic rule setting is carried out on the package deposit of the product, and the application is more flexible.
Referring to fig. 3, the method includes:
in step S301, it is determined whether the behavior data of the user setting up the order is abnormal, and if not, the process proceeds to step S302, and if so, the process proceeds to step S312.
The method and the device can preset a behavior abnormity rule, and judge whether the behavior data of the order set by the user is abnormal or not according to the behavior abnormity rule. For example, the average length of stay per page, the average length of order placement, the path of order placement for products, the path of payment, etc. of the user are checked to determine whether the behavior data of the user setting up the order is abnormal.
In step S302, it is determined whether the user is a login user, and if yes, the process proceeds to step S303, and if no, the process proceeds to step S313.
In step S303, it is determined whether the user history order includes a rejection record, and if yes, the process proceeds to step S316, and if no, the process proceeds to step S304.
If the historical order contains a record of the refusal to pay, it can be determined that the user credit rating is low and the risk is high.
In step S304, it is determined whether the number of valid orders is equal to or greater than the set number, and if yes, the process proceeds to step S308, and if no, the process proceeds to step S305.
The number of times may be set, for example, 3 times, but is not limited thereto, and may be set as needed, for example, 2 times or 4 times.
In step S305, it is determined whether the rental car model is of a set type, and if not, the process proceeds to step S306, and if so, the process proceeds to step S307.
And according to the taxi renting credit data, displaying that the historical orders do not contain the refusal payment records, and the number of times of effective orders is less than the set number of times, determining the credit level, and further judging whether the taxi type is the set type.
The setting type may be, for example, an RV (Recreational Vehicle) type, but is not limited thereto, and may be, for example, an ORV (Off-Road Vehicle) type.
In step S306, the deposit amount is allocated to the user to be greater than the average unit price of the order.
Because the credit rating is medium and the car rental model is not a set type, the deposit amount is allocated to the user to be more than the average unit price of the order.
In this step, the deposit amount can be calculated according to the following formula but is not limited thereto:
deposit amount (user historical order amount/order quantity) 150%
For example, it may also be:
deposit amount is 120% (user history order amount/order quantity)
In step S307, a deposit amount is allocated to the user as a predetermined deposit amount.
Because the credit level is middle and the car renting type is a set type, the deposit amount is distributed to the user to be the preset deposit amount, namely the deposit amount of the product package is not adjusted.
In this step, the deposit amount is allocated to the user as a preset deposit amount, which may be 800USD (american gold) or 1000 USD.
In step S308, it is determined whether the average unit price of the order is larger than the set amount, and if so, the process proceeds to step S311, and if not, the process proceeds to step S309.
And according to the taxi credit data, displaying that the historical orders do not contain the rejection records, and determining that the number of valid orders is greater than or equal to the set number, wherein the credit level is high.
The set amount may be, for example, 500USD (dollar), but is not limited thereto, and may be, for example, 600USD (dollar).
In step S309, it is determined whether the rental car model is of the set type, and if so, the process proceeds to step S310, and if not, the process proceeds to step 311.
The setting type may be, for example, an RV (Recreational Vehicle) type, but is not limited thereto, and may be, for example, an ORV (Off-Road Vehicle) type.
In step S310, a deposit amount is allocated to the user as a predetermined deposit amount minus or set amount.
Because the credit level is high and the car renting type is a set type, the deposit amount is distributed to the user to be a preset deposit amount deduction and exemption set amount.
The set amount may be, for example, 500USD or 400USD, and the deposit amount may be the predetermined deposit amount minus 500USD or minus 400 USD.
In step S311, the deposit amount is allocated to the user as the deposit exemption.
Because the credit rating is high and the average unit price is greater than the set amount, the deposit amount is allocated to the user as the deposit exemption.
Or, because the credit rating is high and the car rental model is not a set type, the deposit amount is allocated to the user as deposit free.
In step S312, the user is allocated a non-open deposit free service.
Because the behavior data of the user for setting up the order is abnormal, the credit level is determined to be low, and the risk is high, so that the user is allocated with the non-open deposit-free service, namely, the deposit-free package is not opened.
In step S313, the user is determined to be a new user.
The user is determined to be a new user because the user is not logged in through an existing account, possibly a guest user.
In step S314, it is determined whether there are multiple valid orders in the mailbox where the user has set up orders, if not, step S315 is entered, and if yes, step S303 is returned to.
Although the user is regarded as a new user, the mailbox records of the user can be further judged, and whether a plurality of valid orders exist in the mailbox for setting the orders by the user can be judged.
In step S315, a deposit amount is allocated to the user as a predetermined deposit amount.
Because the user is a new user and the mailbox for setting the order by the user does not have a plurality of valid orders, the deposit amount allocated to the user in the step is the preset deposit amount, namely the deposit amount of the product package is not adjusted. The preset deposit amount may be 800USD or 1000USD, etc.
In step S316, a deposit amount is allocated to the user as a predetermined deposit amount.
And displaying the history order containing the refusal payment record according to the taxi credit data, determining that the credit level is low, and distributing a strategy of paying according to a preset deposit amount for the user, namely that the deposit amount of the product package is not adjusted.
As can be seen from the embodiment, by using the scheme provided by the embodiment of the application, the car renting risk of the user can be evaluated in advance according to the user behavior; furthermore, different credit levels of different users can be determined according to different taxi renting credit data of the clients, and corresponding different deposit payment strategies are flexibly distributed according to the different credit levels, so that the problem that the taxi of the users is difficult to take is solved, and the willingness of the users to set orders is improved; the risk of order rejection caused by the fact that the user cannot take the vehicle (the order does not appear according to the contract) can be reduced. The term "noshow" generally means that a user has placed an order but has not arrived at a store to pick up a car, and the user has arrived at the store and has not picked up the car due to a certificate or other problems.
Corresponding to the embodiment of the application function implementation method, the application also provides a car rental credit data processing device, equipment and a corresponding embodiment.
Fig. 4 is a schematic structural diagram of a rental car credit data processing apparatus according to an embodiment of the present application.
Referring to fig. 4, the rental car credit data processing apparatus 40 includes: order data acquisition module 41, credit data module 42, deposit policy module 43.
And an order data obtaining module 41, configured to obtain historical order data of the user. Wherein all or part of the historical order data of the user may be obtained from a database, for example, obtaining order data within a half year or a year.
And the credit data module 42 is configured to analyze the execution condition of the historical order data acquired by the order data acquisition module 41 to obtain car rental credit data. The execution condition of the historical order data can be analyzed, including whether a refusal payment record or a deferred payment record appears in the historical order.
And the deposit strategy module 43 is used for distributing different deposit payment strategies for renting the car to the user according to the car renting credit data obtained by the credit data module 42. The method comprises the steps that historical orders including a refusal payment record can be displayed according to taxi credit data, the credit level is determined to be low, and a strategy of paying according to a preset deposit amount is distributed to a user; or, according to the taxi credit data, displaying that the historical order does not contain the refusal payment record, and the number of valid orders is more than or equal to the set number, determining that the credit level is high, and allocating a strategy for adjusting the deposit amount payment for the user. Wherein, the strategy of allocating adjustable deposit amount payment for the user can further comprise: if the average unit price of the order of the user is larger than the set amount, allocating a deposit-free payment strategy for the user; or if the average unit price of the order of the user is less than or equal to the set amount and the car rental model is not the set type, allocating a policy of avoiding deposit payment for the user; or if the average unit price of the order of the user is less than or equal to the set amount and the car renting vehicle type is the set type, distributing a strategy of reducing or avoiding the set amount according to the preset deposit amount for the user.
As can be seen from the embodiment, the car rental credit data processing device provided by the application analyzes the execution condition of the historical order data of the user to obtain car rental credit data; and then according to the car rental credit data, different deposit payment strategies for car rental are distributed to the users. Therefore, for different users, due to different car rental credit data, the car can be taken after the car rental or the deposit is required to reach the set amount uniformly, and different payment strategies such as deposit avoidance or deposit reduction can be flexibly allocated according to different car rental credit data of the users. Therefore, according to the scheme provided by the application, the user can rent the vehicle and take the vehicle more conveniently while controlling the credit risk, the problem that the user is difficult to take the vehicle is solved, and the desire of the user to set up an order on a vehicle renting platform is improved.
Fig. 5 is another schematic structural diagram of the rental car credit data processing apparatus according to the embodiment of the present application.
Referring to fig. 5, the rental car credit data processing apparatus 50 includes: order data acquisition module 41, credit data module 42, deposit strategy module 43, and behavior data module 44.
And an order data obtaining module 41, configured to obtain historical order data of the user.
And the credit data module 42 is configured to analyze the execution condition of the historical order data acquired by the order data acquisition module 41 to obtain car rental credit data.
And the deposit strategy module 43 is used for distributing different deposit payment strategies for renting the car to the user according to the car renting credit data obtained by the credit data module 42.
A behavior data module 44, configured to obtain behavior data of an order set by a user; analyzing according to the behavior data to obtain taxi renting risk data; and the deposit strategy module 43 distributes a deposit strategy for renting the car for the user according to the car renting risk data obtained by the behavior data module 44.
The behavior data of the order set up by the user can include the average residence time per page, the average order placing time, the product order placing path, the payment path and the like of the user. The behavior data module 44 may preset a behavior exception rule according to which whether the behavior data of the order set by the user is abnormal or not is determined. For example, the average stay time per page, the average order placing time, the product order placing path, the payment path and the like of the user are checked to determine whether the behavior data of the order set by the user is abnormal, so as to obtain the car rental risk data. The deposit strategy module 43 can prompt credit risk to be high according to the car renting risk data and allocate unopened deposit-free service to the user; or, according to the car rental risk data, prompting that the credit risk is controllable, and allocating deposit layered payment service for the user.
The deposit strategy module 43 can display the history order including the refusal payment record according to the taxi credit data, determine that the credit level is low, and allocate the strategy of paying according to the preset deposit amount for the user; or, according to the rented credit data, the historical orders do not include the refusal payment record, and the number of valid orders is greater than or equal to the set number, the credit level is determined to be high, and a policy for adjusting the deposit amount payment is allocated to the user, which can be specifically described with reference to the function of the deposit policy module 43 in fig. 4.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 6 is a schematic structural diagram of an electronic device shown in an embodiment of the present application.
Referring to fig. 6, the electronic device 1000 includes a memory 610 and a processor 620.
The Processor 620 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 610 may include various types of storage units, such as system memory, Read Only Memory (ROM), and permanent storage. Wherein the ROM may store static data or instructions that are required by the processor 620 or other modules of the computer. The persistent storage device may be a read-write storage device. The persistent storage may be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the persistent storage device employs a mass storage device (e.g., magnetic or optical disk, flash memory) as the persistent storage device. In other embodiments, the permanent storage may be a removable storage device (e.g., floppy disk, optical drive). The system memory may be a read-write memory device or a volatile read-write memory device, such as a dynamic random access memory. The system memory may store instructions and data that some or all of the processors require at runtime. In addition, the memory 610 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic and/or optical disks, may also be employed. In some embodiments, memory 1010 may include a removable storage device that is readable and/or writable, such as a Compact Disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, dual layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, min SD card, Micro-SD card, etc.), a magnetic floppy disc, or the like. Computer-readable storage media do not contain carrier waves or transitory electronic signals transmitted by wireless or wired means.
The memory 610 has stored thereon executable code that, when processed by the processor 1020, may cause the processor 620 to perform some or all of the methods described above.
Furthermore, the method according to the present application may also be implemented as a computer program or computer program product comprising computer program code instructions for performing some or all of the steps of the above-described method of the present application.
Alternatively, the present application may also be embodied as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) having stored thereon executable code (or a computer program, or computer instruction code) which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform some or all of the various steps of the above-described methods in accordance with the present application.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. A car rental credit data processing method is characterized by comprising the following steps:
acquiring historical order data of a user;
analyzing according to the execution condition of the historical order data to obtain taxi renting credit data;
and distributing different deposit payment strategies for renting the car for the user according to the car rental credit data.
2. The method of claim 1, wherein prior to obtaining the historical user order data, further comprising:
acquiring behavior data of an order set by a user;
analyzing according to the behavior data to obtain taxi renting risk data;
and allocating a vehicle renting deposit strategy for the user according to the vehicle renting risk data.
3. The method according to claim 2, wherein the allocating a car rental deposit policy to the user according to the car rental risk data comprises:
according to the taxi renting risk data, prompting that the credit risk is high, and allocating unopened deposit-free service for the user; or the like, or, alternatively,
and prompting that the credit risk is controllable according to the car renting risk data, and allocating deposit layered payment service for the user.
4. The method according to any one of claims 1 to 3, wherein the allocating different deposit payment policies for rental car to the user according to the rental car credit data comprises:
displaying historical orders containing a refusal payment record according to the taxi credit data, determining that the credit level is low, and distributing a strategy of paying according to a preset deposit amount for a user; or the like, or, alternatively,
and according to the taxi credit data, displaying that the historical order does not contain a refusal payment record, and the number of times of the effective order is greater than or equal to the set number of times, determining that the credit level is high, and allocating a strategy for adjusting the payment of the deposit amount to the user.
5. The method of claim 4, wherein assigning the user a policy for adjustable deposit line payment comprises:
if the average unit price of the order of the user is larger than the set amount, allocating a deposit-free payment strategy for the user; or the like, or, alternatively,
if the average unit price of the user order is less than or equal to the set amount and the car rental model is not the set type, allocating a deposit free payment strategy for the user; or the like, or, alternatively,
and if the average unit price of the order of the user is less than or equal to the set amount and the car renting type is the set type, distributing a strategy for reducing or avoiding the set amount according to the preset deposit amount for the user.
6. The method according to any one of claims 1 to 3, wherein the allocating different deposit payment policies for rental car to the user according to the rental car credit data comprises:
according to the taxi credit data, displaying that the historical order does not contain a refusal payment record, and the number of valid orders is less than the set number, and determining the credit level, then:
if the car rental model is a set type, distributing a strategy of paying according to a preset deposit amount for the user; or the like, or, alternatively,
if the car rental model is not the set type, a strategy of paying according to a deposit amount larger than the average unit price is distributed to the user.
7. The method of claim 2, further comprising:
and when the user is a new user, distributing a strategy of paying according to a preset deposit amount for the user.
8. A rental car credit data processing apparatus, comprising:
the order data acquisition module is used for acquiring historical order data of the user;
the credit data module is used for analyzing according to the execution condition of the historical order data acquired by the order data acquisition module to obtain taxi renting credit data;
and the deposit strategy module is used for distributing different deposit payment strategies for renting the car for the user according to the car renting credit data obtained by the credit data module.
9. The apparatus of claim 8, further comprising:
the behavior data module is used for acquiring behavior data of an order set by a user; analyzing according to the behavior data to obtain taxi renting risk data;
and the deposit strategy module distributes a deposit strategy for renting the car for the user according to the car renting risk data obtained by the behavior data module.
10. An electronic device, comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any one of claims 1-7.
CN202110232915.5A 2021-03-03 2021-03-03 Car rental credit data processing method, device and equipment Pending CN112927045A (en)

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